This year’s annual conference of the Association for Clinical Data Management (ACDM) took place in Dublin, Ireland, right before the 2019 coronavirus (COVID-19) pandemic was declared. During this two-day event (March 9-10), the emphasis was on robotic process automations and artificial intelligence (AI) machines that could be used in streamlining CDM processes. Approaches to more efficiently use patient data streaming from sources such as hospitals dossiers, wearable devices, and social media were also discussed.
During this year’s ACDM conference, which took place before the emergence of the COVID-19 pandemic, current available technologies and new advancements in the field of clinical data management were discussed. The use of current technologies (e.g., app-based tools) during the conference significantly enhanced the productivity of discussions, with questions asked and answered in a real-time manner. This highlighted the recent evolution of technology and its presence/utility in daily life, a point emphasized in all ACDM2020 lectures detailing the wide use of modern tools, and associated troubleshooting, in current and emerging CDM processes.
In line with last year’s conference focus on challenges in handling multistream data capture, Aoife Giblin (CDM Director, ICON) and Ashley Howard (Associate Director CCDM, Pfizer) described novel robotic process automations successfully used at ICON and Pfizer, also touching upon the inherent challenges of automation. In her lecture, Giblin described how the use of wearable devices can speed up the data collection process and the utility of implementing pre-programmed tools that could lock the electronic case report form (eCRF) pages based on pre-defined criteria, without the necessity of involving a data manager in the task. Howard complemented this with a lecture focusing on AI machines that could potentially replace the complex cross-domain manual data review. Both speakers agreed that using advanced technologies for CDM processes has additional benefits, such as reducing manual effort and thus mitigating manual errors. They stressed the fact that by automating the CDM processes, data analysts can effectively and efficiently plan activities and reduce the amount of time spent on lower-value tasks, thus gaining a greater control of the process workflow and available resources. Additionally, speakers highlighted that automation of CDM processes is particularly useful for large trials with large volumes of data collected from multiple sources.
For implementation of new technologies to be successful, relevant key factors majorly contributing to their success need to be considered. Giblin emphasized that the feasibility and additional benefit of using new technologies should be carefully assessed prior to implementation, to ensure that the applied technology will actually add value to the project. The calculated value should justify the investments of time, effort, and costs. While accounting for every real-world scenario during the development phase is unrealistic, Giblin stressed that it is crucial to ensure an effective pilot phase that considers a variety of real-world scenarios, to minimize the number of issues during project implementation. Another important key success factor is time. Sufficient time slots should be budgeted in the project planning process with the aims to: implement users’ feedback before the new processes are rolled out, extensively train users on these new processes, and establish ongoing technical support methods. This will help maximize the successful implementation of new technologies. This point was also emphasized by Howard, who mentioned that the human factor and continuous optimization should not be excluded from these processes. Involvement of data managers is still necessary for providing continuous feedback on the performance of new tools, re-programming them to also perform high-complexity activities in addition to repetitive data cleaning tasks.
In view of the multitude of recent developments in technology, the work environment of data managers will also undergo changes. Tanya du Plessis (CDM, Bioforum) clarified that, in line with the ongoing Information Revolution, these fundamental changes are already taking place. Currently, the number of office-bound employees is decreasing and advancements in technologies allow companies to expand locations worldwide. The rising challenge of this new environment is finding new ways to efficiently and effectively communicate using novel face-to-face technologies. The changing work culture brought about by the new generations of millennials entering the work force and their expectation that technology should be as much as possible used in their daily tasks also adds to the challenge. Du Plessis highlighted that successful management of these young data managers can be achieved by talent-oriented leadership. The changing work force paradigm requires acceptance of the need for remote work and flexibility in working schedules, while simultaneously maintaining team integrity. In this context, using the new technological tools available to integrate employee diversity in work-related processes can help optimize the new dynamic virtual work reality.
Other relevant topics addressed during ACDM2020 were the efficient use of patient data streaming from other sources, such as hospital dossiers (Cliona O’Donovan, Senior Statistician at Pavilion Health), wearable devices (Jennifer Bradford, Head of Data Science at PHASTAR), and social media (Maria Fiorella Pilotti, Clinical Data Team Lead at IQVIA). In addition to the benefits in using this data, the speakers highlighted that there are also challenges in assuring good data quality of routinely collected real-world data.
In concluding the ACDM2020 conference, the positive and negative aspects of technology development were discussed. Significant concerns regarding the future of data managers were addressed, including the major impact of AI on their roles, as well as on dehumanizing the workplace. Nevertheless, it was also highlighted that progressive enhancements in clinical research technologies over recent years led to a positive evolution of data managers’ roles. As mentioned by Sue Huxtable (Senior Director Data Management, Covance) and other conference lecturers, despite the use of Electronic Data Capture (EDC) systems in the last 15-20 years, the data manager’s role has survived. In a world of multiple data sources, the traditional role of the data manager is now moving away from an EDC-based environment and towards a reconciliation and central expert role.
In light of the restrictions imposed due to the spread of Corona virus, working from home-based offices requires adjustments in mindsets in relation to the working environment and intensive use of the new technologies discussed during ACDM2020. While pharmaceutical companies running a multitude of trials with large patient populations will directly benefit from the recent AI advancements, a balance between the efforts necessary for their implementation versus utility needs to be reached for companies running Phase I-II clinical trials, with low numbers of patients.
We at CATO SMS are employing cutting-edge technologies, such as state-of-the-art tools for virtual research (e.g. THREAD) or SAS reports for refined statistical data evaluation and development of Insights & Analytics for informative perspectives on clinical trials in relation to the clinical trial landscape. Using our “know-how” of all facets of oncology drug development and clinical trial conduct, our data scientists work together with each department to put the data into context for interpretation, and provide our proprietary Insights & Analytics reports at regular intervals during the trial.
With these and additional technologies, and by actively following trends in AI developments, we are continuously refining our CDM processes to improve early- and late-phase clinical trials for the benefit of our current and future sponsors.
- Dublin, March 2020